Energy management systems are generally used to manage the production and use of energy within, for example, an industrial power generation plant, an industrial manufacturing or production plant, a municipal plant, etc., in an attempt to assure adequate operation of the plant/community in response to unforeseen or unexpected events. In some limited instances, simplistic energy management systems have been used to manage the use and therefore the cost of energy within a plant. However, before deregulation of the power companies and the rise of the Independent Power Producer (IPP) program, energy management was primarily a concern of the industrial customer. As a result, outside of industrial uses, energy management systems are fairly simplistic in nature, taking the form of, for example, programmable thermostats used in residences, etc.
While industrial energy management systems currently exist in many forms, these energy management systems are limited in scope, are still fairly simple in nature and are not configured to determine the energy savings that might be obtained through a detailed analysis of energy production and usage costs in a particular plant configuration or situation. Thus, even the industrial energy management systems in use today do not obtain the energy cost savings that might be had in situations that can create use and/or sell energy in various forms, using various different types of plant equipment.
The most common use of industrial energy management systems is as a load shedding system within industrial manufacturing plants, which have had automatic load shedding systems for a long time. In general, load shedding systems determine the amount of load (plant equipment drawing power) that must be almost instantaneously removed from operation to keep the remaining portions of the industrial plant operational. Load reduction or shedding is typically performed in response to a system disturbance (and the consequent possible additional disturbances resulting from a primary system disturbance) that results in a power generation deficiency condition. Common system disturbances that can cause load shedding include equipment faults, loss of power generation equipment, switching errors, lightning strikes, etc. Industrial plant energy management systems respond to these conditions by employing any of a number of advanced schemes that determine which loads to shed at any particular time in response to a particular type of disturbance or event. In some cases, blocks of loads are turned off or loads may be shed based on a preset priority that can be modified. In some instances, neural networks have even been used to determine the order in which loads should be shed.
However, energy management systems in the form of load shedding systems are generally limited to turning off loads within the plant, and do not decide when or how to restart or reconnect loads within the plant. In fact, the reclosing of electrical breakers and the restoring of the loads in an industrial plant, after the breakers have been automatically opened by a load shedding system, has traditionally been performed manually. Restoring loads manually is not so cumbersome when it only has to be performed when a load shed was caused by an electrical disturbance, because these events do not occur that frequently within an industrial plant operating environment.
However, as electric power becomes a larger and larger portion of the cost of production within an industrial plant, it will be necessary to decide when to run the plant production equipment and when to idle the plant production equipment based on the economics of energy management. The increasing cost of energy (including the costs associated with electric and fossil fuel based energy generation) will make current production plants less competitive unless the industrial producers adapt. For example, it may be necessary, in some situations, to shift or to curtail production and large energy consumption operations in an industrial plant to off peak hours when electric rates are lower, so as to keep the plant running competitively. These types of determinations will lead to loads being shed and restored on a more frequent basis, as once the price of power is at a point where production can be economically resumed, it is advantageous to start production as quickly as possible, and so not to have to wait for the operators to manually restart the loads. Likewise, when loads can start being restored, the most critical ones should be restored first. This decision process makes the manual load restoration process even slower, resulting in loss of production.
Most industrial plants, as well as other energy consumers that use electrical power, typically rely at least in part on the public power grid, which is designed to provide electrical power or energy at any needed time. This electrical grid is, in turn, fed by numerous power plants or other power suppliers that operate to provide electric power to the grid based on forecasted demands or required loads. A typical power plant can produce energy using multiple different types of energy generation systems including, for example, steam powered turbine systems, fossil fuel turbine systems, nuclear power generation systems, wind powered generators, solar powered generators, etc. Currently, these power generation systems operate by producing a desired demand as currently forecast or needed by the power grid. However, these power generation plants generally use only simple techniques to optimize the running of the power plant so as to provide the required power. These optimization techniques may, for example, decide whether to run one or two boilers, which boiler system to run first based on their respective efficiencies, whether to provide power at the current time at all based on the going rate being paid for electricity, etc. Generally speaking, the decisions as to whether to run a power plant and/or what specific components of the power plant to run in order to provide the electrical energy, as well as the amount of electrical energy to produce, are made by power plant operators who use basic or general criteria, such as those expressed by rule of thumbs, to determine the best or “most optimal” manner of running the plant at the highest profitability. However, these plants could benefit from an energy management system that operates to determine the best set of equipment to run at any particular time to maximize the operating profit of the plant.
In a similar manner, users of the electrical power from the power grid, like industrial plants, municipal plants, residential or commercial properties, etc., can benefit from better energy management systems. In many cases, these entities are both consumers and producers of energy. For example, many industrial plants, in addition to obtaining electrical energy from the power grid, produce some of the energy they use, convert energy from one form to another form and/or are capable of storing energy to some degree. For example, many industrial plants, municipal plants, etc., include plant equipment that requires steam to operate. Thus, in addition to obtaining electrical energy from the power grid, these plants include power generating equipment such as boiler systems that consume other raw materials, like natural gas, fuel oil, etc. to operate. Likewise, many municipal plants, such as municipal heating plants, water treatment plants, etc, and many residential plants, such as college campuses, commercial buildings, groups of buildings in an industrial or research park, etc., have both power generation equipment and power consuming equipment. For example, many college campuses, city or other municipal systems, etc. use steam for heating purposes at certain times while, at other times, run electrically driven air conditioning systems, to provide cooling. These plants may include power generating equipment, such as oil and gas fired boilers, and these plants may additionally include power storage systems such as thermal chillers, batteries or other equipment that is capable of storing energy for use at a later time.
In these types of plants, operators, at best, tend to manage the creation, distribution and use of energy using a set of fairly basic or simplistic rules of thumb in an attempt to reduce overall energy costs. For example, operators may attempt to save on energy costs by shutting down certain systems or running these systems at a minimum level within the plant when the systems are not needed as much. In one example, the boilers used to create steam for heating purposes in a college campus may be shut down or may be run at a minimal level during the summer months, during weekends, or during spring or semester breaks when fewer students are present. However, because the operators of these systems only use basic or simplistic rules of thumb for altering the operation of the plant to save on energy costs, the operators quickly lose the ability to determine the best or most optimal methodology of running the plant equipment (including equipment that may create energy in various forms, use energy in various forms, convert energy from one form to another form, or store energy in various forms) so as to reduce the overall costs of energy within the plant. This problem is exacerbated by the fact that the operators do not typically know the exact cost of running any particular piece or set of equipment at any particular time because the costs of the energy from the power grid, natural gas costs, etc. change regularly, and may change significantly even during a single day.
Still further, while power plants are specifically designed to create and sell energy to the power grid, many other types of industrial plants, such as process plants, municipal plants, etc. can now sell energy that they create to a power grid or to another consumer. The operators of these systems, however, do not typically have enough knowledge or experience to be able to determine if it is more cost efficient to shed loads to reduce the consumption of energy at a plant, to maintain loads or to reconnect loads so as to run the plant at optimum loading for production purposes or to create more energy than is currently needed and to sell that energy to a third party, such as to the power grid. In fact, in many cases, it may actually be more efficient for a particular plant to stop production and to instead use the plant equipment to create energy and sell this energy to a third party via the power grid.
As will be understood, there are many factors to consider when optimizing (e.g., minimizing) the costs of energy creation and usage in a particular industrial, municipal or residential plant, including the forms of energy (electrical, steam, etc.) that can be or that need to be created at any particular time, the amount of energy in each of these forms that needs to be used to run plant equipment at various operational levels, the operational levels at which the components of the plant need to be run at any particular time to meet the business purposes of the plant, the costs of the raw materials needed to create and/or store energy at the plant, the cost of the energy purchased from the power grid or other third parties, whether there is an ability to store energy at the plant for later use or sale, the energy efficiencies of the plant equipment (including any energy storage equipment), etc. Energy optimization is further complicated by the fact that the plant needs and the energy costs can change drastically over short periods of time, and that forecasting energy costs is thus a necessary part of any energy management system that attempts to minimize or otherwise optimize energy costs over time. Because these factors are constantly fluctuating, plant operators quickly lose the ability to make the complicated and very involved calculations needed to determine the set of plant operational conditions that optimizes the costs/profits of the plant taking energy usage into account within the plant. Thus, while plant operators can make gross changes to the operational parameters of a plant in an to attempt to reduce the energy costs of the plant, operators really can not determine the best manner of running a plant over time to minimize energy costs using current energy managements systems, as it is almost impossible to manually calculate or determine the most optimal manner of running the plant at any particular time, much less over a time period extending into the future.